Text data analysis of social media is becoming more and more important since it includes the most recent information on what people think about. Likewise, emotion is one of the most valuable parts of human communication, emotion analysis is a type of information extraction process which identifies the emotional states of a given text. In this study, we investigated the performance of deep neural networks on emotion analysis from Turkish tweets. For this, we examined three different deep learning architectures including artificial neural network (ANN), convolutional neural network (CNN) and recurrent neural network (RNN) with long short-term memory (LSTM). Besides, we curated a dataset of Turkish tweets and annotated each tweet automatically for six emotion categories using a lexicon-based approach. For the evaluation, we conducted a set of experiments for each architecture. The results showed that the lexicon- based automatic annotation of tweets is valid. Secondly, ANN produced the worst result as expected, and CNN resulted in the highest score of 0.74 in terms of accuracy measure. Experiments also showed that our proposed approach for emotion analysis of tweets in Turkish performs better than state-of-the-art in this topic.
EXISTING SYSTEM:
Twitter plays an important role in providing raw data to be used in sentiment and emotion analysis. In literature, there are many studies about sentiment [14]–[17] and emotion analysis [18]–[20] on Twitter data in many languages including Turkish [6]. However, most studies in Turkish text deals with sentiment analysis rather than emotion analysis. So, in this section, we provide the state-of-the-art in sentiment and emotion analysis of Twitter data in both English and Turkish. In the literature, some studies dealing with sentiment analysis of Twitter data in Turkish. Coban et al., [21] focused on analysing sentiment extraction from social media sources. So, they first collected a dataset composed of 14,777 tweets.
EXISTING SYSTEM DISADVANTAGES:
1.LESS ACCURACY
2. LOW EFFICIENCY
PROPOSED SYSTEM :
The dataset we curated, which we named as Turkish Twitter emotion dataset, contains Turkish tweets from Twitter which is commonly used social networking and microblogging ser- vice. In Twitter, registered users can read and post messages about every conceivable subject that is named as tweets. Each tweet has a limit of 280 characters and also its context can be soloistic, from daily lives to the developments in nature and society. So, people express their emotions about these developments around them in tweets. The datasets created with tweets may contain a lack of context, spelling errors on purpose or not, slang and repeating characters
PROPOSED SYSTEM ADVANTAGES:
1.HIGH ACCURACY
2.HIGH EFFICIENCY
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
